mnist是经典的深度学习入门的task，tensorflow中也有相关的例子，但是都是用初级的api实现的，性能也不怎么样，这里我们用slim模块训练mnist，秒上99%
需要注意的一点是使用batchnorm层的话需要把normalizer_params中的"updates_collections"设为None，或者单独设置一个update_ops，使得train_op依赖于update_ops。
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import tensorflow.contrib.slim as slim
mnist = input_data.read_data_sets("/tmp/mnist/", one_hot=True)
# conv1 conv2 fc acc all with batchnorm
# 1 2 100 0.97
# 2 4 100 0.9844
# 4 8 100 0.9894
# 6 12 100 0.9912
# 8 16 100 0.9904
# 6 12 50 0.9912
# 6 12 200 0.9922
# 32 64 200 0.9929
# 32 64 500 0.9919
#* 128 256 100 0.9955
#without batchnnorm
# 6 12 100 0.9841
#make it biggger if your gpu memory is not enough
test_batch_size=100
def lenet(input,is_training=True):
net=slim.conv2d(input,6,[5,5],scope="conv1")
net=slim.max_pool2d(net,[2,2],scope="pool1")
net=slim.conv2d(net,12,[5,5],scope="conv2")
net=slim.max_pool2d(net,[2,2],scope="pool2")
net=slim.flatten(net)
net=slim.fully_connected(net,1024,scope="fc1")
net=slim.dropout(net,is_training=is_training,scope="dropout")
net=slim.fully_connected(net,10, activation_fn=None, normalizer_fn=None,scope="fc2")
return net
def lenet_batchnorm(input,is_training):
batch_norm_params={"is_training":is_training,"decay":0.9}
#, "updates_collections": None
with slim.arg_scope([slim.conv2d,slim.fully_connected],weights_regularizer=slim.l2_regularizer(0.0005),
normalizer_fn=slim.batch_norm,normalizer_params=batch_norm_params):
net=slim.conv2d(input,6,[5,5],scope="conv1")
net=slim.max_pool2d(net,[2,2],scope="pool1")
net=slim.conv2d(net,12,[5,5],scope="conv2")
net=slim.max_pool2d(net,[2,2],scope="pool2")
net=slim.flatten(net,scope="flatten")
net=slim.fully_connected(net,100,scope="fc3")
net=slim.dropout(net,is_training=is_training,scope="dropout")
net=slim.fully_connected(net,10,activation_fn=None,normalizer_fn=None,scope="prob")
return net
def train():
is_training = tf.placeholder(tf.bool, name='MODE')
x=tf.placeholder(tf.float32,shape=[None,784])
y_=tf.placeholder(tf.float32,shape=[None,10])
image=tf.reshape(x,[-1,28,28,1])
with tf.name_scope("image"):
tf.summary.image("image",image)
y=lenet_batchnorm(image,is_training)
#y = lenet(image, is_training)
loss=tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y)#slim.losses.softmax_cross_entropy(y,y_)
global_step=slim.get_or_create_global_step()#tf.Variable(0)
learning_rate=tf.train.exponential_decay(1e-4,global_step*100,50000,0.95,staircase=True)
#train_op=tf.train.AdamOptimizer(learning_rate).minimize(loss,global_step=global_step)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = tf.train.AdamOptimizer(0.01).minimize(loss, global_step=global_step)
correct_prediction=tf.equal(tf.argmax(y,axis=1),tf.argmax(y_,axis=1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
tf.summary.scalar("acc",accuracy)
merged_summary=tf.summary.merge_all()
saver=tf.train.Saver()
with tf.Session()as sess:
writer=tf.summary.FileWriter("log",sess.graph)
sess.run(tf.global_variables_initializer())
for i in range(10000):
batch=mnist.train.next_batch(100)
if i%100==0:
summary,train_acc=sess.run([merged_summary,accuracy],feed_dict={x:batch[0],y_:batch[1],is_training:False})
writer.add_summary(summary,i)
print(str(i)+":"+str(train_acc))
sess.run(train_op,feed_dict={x:batch[0],y_:batch[1],is_training:True})
#eval_acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels,is_training:False})
acc = 0
for batch in range(10000/test_batch_size):
batch = mnist.test.next_batch(test_batch_size)
acc += sess.run(accuracy, feed_dict={x:batch[0],y_:batch[1], is_training:False})
eval_acc=acc/(10000/test_batch_size)
print("test acc"+str(eval_acc))
saver.save(sess,"models/lenet.ckpt",global_step=global_step)
if __name__=="__main__":
train()运行结果:
8200:0.99
8300:1.0
8400:1.0
8500:1.0
8600:0.99
8700:0.98
8800:0.99
8900:1.0
9000:0.98
9100:0.99
9200:0.99
9300:1.0
9400:1.0
9500:1.0
9600:1.0
9700:0.99
9800:1.0
9900:1.0
test acc0.9916000074148178